SECONDARY PULMONARY TUBERCULOSIS RECOGNITION BY ROTATION ANGLE VECTOR GRID-BASED FRACTIONAL FOURIER ENTROPY

Shui-Hua Wang (), Yeliz Karaca (), Xin Zhang and Yu-Dong Zhang
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Shui-Hua Wang: School of Mathematics and Actuarial Science, University of Leicester, University Road, Leicester LE1 7RH, UK
Yeliz Karaca: ��University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA
Xin Zhang: ��Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Huai’an, Jiangsu 223002, P. R. China
Yu-Dong Zhang: �School of Informatics, University of Leicester, University Road, Leicester LE1 7RH, UK

FRACTALS (fractals), 2022, vol. 30, issue 01, 1-17

Abstract: Aim: Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis bacteria. This study plans to build a novel deep learning-based model for the accurate recognition of tuberculosis. Methods: We propose a novel model — rotation angle vector grid-based fractional Fourier entropy and deep stacked sparse autoencoder (RAVG-FrFE–DSSAE) — which uses RAVG-FrFE as a feature extractor and harnesses DSSAE as the classifier. Moreover, an 18-way MDA is introduced on the training set to avoid overfitting. Results: Experimental results of 10 runs of 10-fold CV showcase that this proposed RAVG-FrFE–DSSAE algorithm yields a reasonable performance including of 93.68±1.11% sensitivity, 94.38±1.11% specificity, 94.35±1.04% precision, 94.03±0.69% accuracy, 94.01±0.70% F1-score, 88.07±1.38% MCC, 94.01±0.70% FMI, and 0.9725 AUC, respectively. Conclusions: Our result outperforms the eight state-of-the-art approaches. Besides, the result shows the effectiveness of the 18-way MDA.

Keywords: Deep Learning; Rotation Angle Vector Grid; Fractional Fourier Entropy; Fractional Fourier Transform; Deep Stacked Sparse Autoencoder; Secondary Pulmonary Tuberculosis; Recognition; Multiple-Way Data Augmentation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22400473

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